Previously, understanding of the fluvial process from the ecological, morphological, and hydrodynamic perspectives has largely relied on the limited scale of in-situ field observation or the sparse spatial and temporal scale of satellite-based remote sensing. However, with the recent advent of unmanned aerial vehicles (UAVs) and concurrent advances in sensor technology, measurement campaign has been revolutionized and the view of rivers has fundamentally changed from the local scale to the holistic scale; the perspective has shifted from a static to a dynamic one. UAVs can provide a fine spatial and temporal resolution of measurements with a relatively low cost, which, as compared to conventional satellite or pilot-controlled airborne systems, can be more suitable for the analysis of fluvial processes in narrow rivers and small lakes. In this paper, we comprehensively review and document various crucial achievements driven by UAVs-based remote sensing in fluvial environments, among a variety of other relevant applications. Specifically, the paper highlights the UAV-based fluvial remote sensing in terms of riparian vegetation, hazardous aquatic algae blooms, submerged morphology, water-surface slope, sediment, flow velocity, and disasters, including flood inundation mapping.

Conventional streamflow monitoring methods entail one-to-one relationships between two flow variables obtained by combining direct flow measurements with statistical analyses. These relationships (i.e. ratings) are used to monitor both steady and unsteady flows despite that in the latter cases the flow variables display an inherent hysteretic behaviour. Such behaviour is prominent if the wave passing through the gauging station is non-kinematic. This paper demonstrates that the index-velocity and continuous slope-area methods are more suitable to monitor unsteady flows in comparison with the widely used stage–discharge approach. Case studies are presented to show that, contrary to current perceptions in practical applications, hysteresis can be captured even in small streams and frequently-occurring run-off events. The paper also highlights the separation of the flow variable hydrographs in unsteady flows. This hysteresis-related aspect is less investigated so far despite having important practical implications for both hydrometric and fluvial transport applications.

Streamflow forecasting generally relies on coupled rainfall-runoff-routing models calibrated and executed with data estimated by monitoring protocols that do not fully capture the dynamics of unsteady flows. This limits the ability to accurately forecast flood crests and issue hazard warnings. Here we utilize directly measured datasets acquired for streamflow estimation to develop a data-driven forecasting algorithm that does not require conventional physically-based modeling. We test the potential of our algorithm using measurements acquired at an index-velocity gaging station on the Illinois River, USA, between 2014 and 2019. We find that the forecasting protocol is able to deliver short-term predictions of flood crest magnitude and arrival time. The algorithm produces better agreement with larger events and is more reliable for single-peak storms possibly due to the prominence of hysteretic behavior in such events. We conclude that flood hazard can be forecast using directly measured index-velocity and stage alone.

Passive remote sensing is a practical and widely used method for bathymetry mapping in shallow rivers. However, the accuracy of this approach is limited because of different riverbed types; therefore, it is important to classify the riverbed types for improving bathymetry mapping accuracy and providing useful information for fluvial systems. In this study, we proposed a Gaussian mixture model (GMM)-based clustering method that utilizes hyperspectral imagery to classify riverbed types without sampling the bed material. We evaluated the proposed method in two shallow streams with different bed mixture conditions: (i) sand and vegetation and (ii) sand and moss-covered sand. The results showed that the GMM method accurately identified the spectral variability caused by diverse riverbed materials, enabling the precise classification of riverbed types. Moreover, by combining the GMM method with optimal band ratio analysis, we observed a reduction in error for the bathymetry mapping results by approximately 0.05 to 0.07 m. While our proposed method exhibits potential applications in various river environments, further research is needed to validate its effectiveness in classifying more complex riverbed types and conditions. Overall, our study findings suggest that the GMM-based clustering method using hyperspectral imagery is a promising tool for improving bathymetry mapping accuracy and classifying riverbed types in shallow rivers.

Recent advances in instruments are transforming our capabilities to better understand, monitor, and model river systems. The present paper illustrates such capabilities by providing new insights into unsteady flows captured with a Horizontal Acoustic Current Profiler (HADCP) integrated at an operational index-velocity gaging station. The illustrations demonstrate that the high-resolution stage and velocity measurements directly acquired during flood wave propagation reveal the intricate interplay among flow variables that are essential for better supporting judicious decision making for river management, flooding, sediment transport, and stream ecology. The paper confirms that the index-velocity method better captures the unsteady flow dynamics in comparison with the stage-discharge monitoring approach. At a time when the intensity and frequency of floods is continuously increasing, a better understanding of the critical features of flood waves during extreme events and the possibility of capturing more accurately their dynamics in real time is of special socio-economic significance.

The goal of this study is to evaluate and rank the effectiveness of the Korean governments’ Four Major River Restoration Projects. A hydraulic simulation model and geographic information system tools are used to analyze the flood mitigation effects provided by these projects. In addition, the rankings of four major rivers are derived using two sensitivity analyses to weight values and performance measures for Multi-Attribute Decision Making (MADM) technique. As a result, the flood risk presented by the Nakdong River can be most effectively decreased when considering these weights, and levee freeboards are the most critical criterion in the effectiveness ranking. The Youngsan River’s flow capacity criterion is also critical for this problem. This study will be helpful in the robust prioritization of various water resources plans.